Striim for Azure Synapse Analytics

Continuous, Real-Time Data Movement

Move data to Azure Synapse Analytics in real time from a wide variety of data sources

The Striim platform, running in the Azure Cloud, offers non-intrusive, real-time data ingestion from data warehouses (including Oracle Exadata, Teradata, Amazon Redshift), databases (including Oracle, SQL Server, HPE NonStop, and MySQL), log files, messaging systems, sensors, and Hadoop solutions with in-flight transformations and optimized delivery.

By delivering continuously updated business data to Azure Synapse Analytics, Striim eases cloud-based analytics and supports operational decision making on a continuous basis.

Why Striim for Azure Synapse Analytics

Striim offers a secure, reliable, and scalable service for real-time collection, preparation, and movement of unstructured, semi-structured, and structured data into Azure Synapse Analytics. It enables phased, zero database downtime migration to Azure Synapse Analytics from existing data warehouses by running them in parallel. For real-time data movement from enterprise databases, Striim uses low-impact change data capture (CDC) to avoid any modification or performance impact on source production systems.

Via in-line transformations, including denormalization, before delivering to Azure Synapse Analytics  it reduces on-premises ETL workload as well as data latency. Striim enables fast data loading to Azure Synapse Analytics through optimized interfaces such as streaming (JDBC) or batching (PolyBase). Azure customers can store the data in the right format, and provide full context for any downstream operations, such as reporting and analytical applications.

Real-Time Operational Store and Data Warehousing
  • Streamline your data architecture to gain more operational value from your modern analytics solution
  • Set up real-time data pipelines to create an operational data store within the Azure Synapse Analytics and offload operational reporting
  • Avoid batch ETL related inefficiencies using non-intrusive CDC combined with in-flight data processing,
  • Update traditional ETL processes with streaming data integration to fully benefit from next-gen cloud-based analytics
Streaming Integration for Big Data Analytics
  • Use real-time collection, preparation and delivery into Azure Databricks, HDInsight, and Cosmos DB to support advanced analytics
  • Deliver real-time data continuously into Azure Storage (ADLS, Blob, ADLS Gen 2) from on-premise apps
  • Process data-in-motion to speed time-to-insight and minimize latency, while reducing on-premise ETL efforts
  • For long-running, Spark-based transformations, use Striim with Azure Databricks

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